Data analysis
Course: System Analysis
Structural unit: Faculty of Computer Science and Cybernetics
Title
Data analysis
Code
Module type
Обов’язкова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
6 Semester
Number of ECTS credits allocated
4
Learning outcomes
Know and understand the basic sections and tasks of data analysis.
Be able to use the basic 40 methods and tools from all sections of data analysis.
Demonstrate the ability to self-study and continue professional development.
Be able to organize their own activities and get results within a limited time.
Demonstrate skills of interaction with other people, ability to work in teams.
Form of study
Distance form
Prerequisites and co-requisites
Know: probability theory, probability processes and mathematical statistics.
Be able to: apply knowledge of probability theory and mathematical statistics.
Have basic skills: solve problems in probability theory and mathematics statistics.
Course content
The discipline has the following sections: Data processing. Correlation analysis. Regression analysis. Analysis of variance. Covariance analysis. Time series analysis. Classification problems. The main task is to provide students with basic knowledge of the whole arsenal of methods and tools in all major sections of data analysis and gain experience in working with relevant software in solving applications. Uses concepts from probability theory and
mathematical statistics, mathematical analysis and algebra. Acts as a base for the disciplines: actuarial mathematics, a number of disciplines of free choice of the student (by blocks), and will also be useful in writing final theses of bachelors and masters. Discipline is a compulsory subject.
Recommended or required reading and other learning resources/tools
1. Afifi A. Statistical analysis. Computer-based approach / A. Afifi, S. Eisen. - M .: Mir, 1982.
2. Brandt Z. Data analysis / Z. Brandt. - M .: Mir, 2003.
3. Draper N. Applied regression analysis / N. Draper, G. Smith. - 3rd edition. - К .: Диалектика, 2007.
4. Applied Statistics: Fundamentals of Modeling and Primary Data Processing / SA Aivazyan et al. - Moscow: Finance and Statistics, 1983.
5. Applied Statistics: The study of dependencies / SA Aivazyan et al. - Moscow: Finance and Statistics, 1985.
6. Applied Statistics: Classification and reduction of dimensions / SA Aivazyan et al. - Moscow: Finance and statistics, 1989.
7. Slabospitsky OS Data analysis. Preliminary processing: textbook. way. / OS Slabospitsky. - Kyiv: Ukrainian Orthodox Church "Kyiv University", 2001.
8. Slabospitsky OS Fundamentals of correlation data analysis: textbook. way. / OS Slabospitsky. - Kyiv: Ukrainian Orthodox Church "Kyiv University", 2006
Planned learning activities and teaching methods
Lectures, practical classes, independent work
Assessment methods and criteria
Test work, exam.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline